Energy-aware Real-time Scheduling Research Articles

Energy-Aware Real-Time Scheduling of Multiple Periodic DAGs on Heterogeneous Systems

Many of today’s complex Cyber-Physical Systems (CPSs) are represented as a set of independent co-executing real-time control applications, where each such application is represented as a precedence-constrained task graph. The applications execute in infinite loops, periodically acquiring data from the environment through sensors at a particular frequency, processing the same, and then producing processed data via actuators. These CPSs often execute under stringent resource constraints (such as limited energy budgets) in distributed networked environments and may be heterogeneous to be able to satisfactorily meet stipulated performance specifications. This work presents a list-based energy-aware scheduler called DPMRS for a set of real-time control applications co-executing in a heterogeneous distributed environment. DPMRS introduces a novel approach for the integrated behavioural representation of a set of co-executing real-time DAG-structured applications. Each task in this integrated representation is then scheduled by determining its relative execution start time on a particular processor, which operates at an appropriately chosen frequency when the task runs on this processor. The overall objective of DPMRS is to minimize aggregate energy consumed in the execution of all tasks. The efficacy of the proposed scheduler has been exhibited through extensive simulation experiments using benchmark task graphs from different application domains. Additionally, a case study on automotive control systems has been included to show the applicability of the proposed work in real world settings.

Energy-Aware Nonpreemptive Scheduling of Mixed-Criticality Real-Time Task Systems

Energy-aware real-time scheduling for mixed-criticality (MC) systems with different criticality levels has drawn many researchers’ attentions. However, most of the studies focus on the preemptive MC task model and few studies consider the nonpreemptive MC task model, in which all jobs cannot be preempted until completion. In this article, we address the energy minimization problem for MC systems with nonpreemptive dynamic priority scheduling. First, we develop schedulability test of nonpreemptive earliest deadline first (NP-EDF) in single processor MC systems. Second, we extend the results to nonpreemptive earliest deadline first with virtual deadline (NP-EDFVD), which is the first attempt for nonpreemptive dynamic priority scheduling in single processor MC systems. Third, the energy-aware nonpreemptive scheduling algorithm (EANPS) based on NP-EDFVD is proposed to solve the energy minimization problem for MC systems with nonpreemptive dynamic priority scheduling. Finally, an industrial use-case and extensive simulations are used to validate the performance of the proposed algorithm, and the experimental results show that the EANPS algorithm consumes average 25.72% less energy than that of NP-EDFVD.

Energy-Efficient Parallel Real-Time Scheduling on Clustered Multi-Core

Energy-efficiency is a critical requirement for computation-intensive real-time applications on multi-core embedded systems. Multi-core processors enable intra-task parallelism, and in this work, we study energy-efficient real-time scheduling of constrained deadline sporadic parallel tasks, where each task is represented as a directed acyclic graph (DAG). We consider a clustered multi-core platform where processors within the same cluster run at the same speed at any given time. A new concept named speed-profile is proposed to model per-task and per-cluster energy-consumption variations during run-time to minimize the expected long-term energy consumption. To our knowledge, no existing work considers energy-aware real-time scheduling of DAG tasks with constrained deadlines, nor on a clustered multi-core platform. The proposed energy-aware real-time scheduler is implemented upon an ODROID XU-3 board to evaluate and demonstrate its feasibility and practicality. To complement our system experiments in large-scale, we have also conducted simulations that demonstrate a CPU energy saving of up to 67 percent through our proposed approach compared to existing methods.

Dynamic harvesting- and energy-aware real-time task scheduling

Energy harvesting, along with effective storage of the energy, is a very common approach to attain sustainable computing in today’s embedded systems. Employing a hybrid energy storage system (HESS), which constitutes of two or more types of energy storage systems (ESSs), helps to compensate for the weaknesses of one ESS type using the strengths of another type. The capacity of an ESS, and thus that of a HESS, can be modeled by dividing it into Instantly Available Charge (IAC) and Instantly Unavailable Charge (IUC) parts; the existing charge in an ESS always flows from the part with higher voltage to the other one. The main idea of this study is to intelligently control the flows in the HESS to maximizing either the IAC or the IUC charge. We propose the HLPF real-time task scheduling algorithm to do so through deciding to execute the tasks in the ascending or descending order of their power requirements. Extensive simulations show impressive lifetime improvements of up to 20 % in comparison to the classical real-time task scheduling algorithms.

Fault-Tolerant Energy-Aware Task Scheduling on Multiprocessor System for Fixed-Priority Real-Time Tasks

Energy-aware real-time scheduling is gaining attention in recent years owing to environmental concerns and applications in numerous fields. System reliability also gets affected adversely with increasing energy dissipations posing serious challenges before the researchers. Keeping these in view, in recent times researchers have diverted to combining issues of fault-tolerance and energy efficiency. In literature, DVFS and DPM, most commonly used techniques for power management in task scheduling, are often combined with Primary/Backup technique to achieve fault tolerance against transient and permanent faults. Optimal algorithms, Earliest deadline first (EDF) and Rate-Monotonic (RM), meant for scheduling dynamic and fixed priority tasks respectively, have mainly been analyzed using a dual-processor approach for fault-tolerance and energy efficiency. In this paper, to handle higher workload of fixed-priority real-time tasks, energy-aware fault-tolerant scheduling algorithms are proposed for multiprocessor systems with balanced and unbalanced number of main and auxiliary processors. Simulations over extensive task-sets indicate that balanced approach is more energy-efficient than the unbalanced one.

Real-time and energy efficiency in Linux

The recent changes made in the Linux kernel aimed at achieving better energy efficiency through a tighter integration between the CPU scheduler and the frequency-scaling subsystem. However, in the original implementation, the frequency scaling mechanism was used only when there were no realtime tasks in execution. This paper shows how the deadline scheduler and the cpufreq subsystem have been extended to relax this constraint and implement an energy-aware realtime scheduling algorithm. In particular, we describe the design issues encountered when implementing the GRUB-PA algorithm on a real operating system like Linux. A set of experimental results on a multi-core ARM platform validate the effectiveness of the proposed implementation, which has been recently merged into the official Linux kernel.

Energy-Efficient Real-Time Scheduling of DAG Tasks

This work studies energy-aware real-time scheduling of a set of sporadic Directed Acyclic Graph (DAG) tasks with implicit deadlines. While meeting all real-time constraints, we try to identify the best task allocation and execution pattern such that the average power consumption of the whole platform is minimized. To our knowledge, this is the first work that addresses the power consumption issue in scheduling multiple DAG tasks on multi-cores and allows intra-task processor sharing. First, we adapt the decomposition-based framework for federated scheduling and propose an energy-sub-optimal scheduler. Then, we derive an approximation algorithm to identify processors to be merged together for further improvements in energy-efficiency. The effectiveness of the proposed approach is evaluated both theoretically via approximation ratio bounds and also experimentally through simulation study. Experimental results on randomly generated workloads show that our algorithms achieve an energy saving of 60% to 68% compared to existing DAG task schedulers.

A new high-performance open CNC system and its energy-aware scheduling algorithm

Computer numerical control (CNC) systems are shifting to a direction of open architecture which has better flexibility, adaptability, versatility, and expansibility. Existing CNC systems tend to have a high level energy consumption. This paper introduces a new open CNC system based on the low-power embedded platform, named open and high-performance CNC (OHP-CNC). OHP-CNC is able to achieve high precision, high efficiency, and low power consumption by making use of international standards, open components such as hardware and software, and an energy-aware real-time scheduling algorithm. The proposed algorithm for mixed tasks, including periodic and aperiodic tasks, is divided into two phases. Firstly, the slack time and utilization are calculated on each processor and tasks are assigned to the processor according to the load. Secondly, because there is a trade-off between the energy-saving and the response times of the aperiodic task, the scheduling server is used to schedule aperiodic tasks in order to meet the response time constraints of aperiodic tasks. Meanwhile, periodic tasks recycle the slack time with dynamic voltage scaling technology to achieve low power consumption. Experiment results show that the energy-aware real-time algorithm yields high-performance and effective machining processes.

Energy Efficient Real-Time Task Scheduling for Embedded Systems with Hybrid Main Memory

Available energy becomes a critical design issue for the increasingly complex real-time embedded systems. Phase Change Memory (PCM), with high density and low idle power, has recently been extensively studied as a promising alternative of DRAM. Hybrid PCM-DRAM main memory architecture has been proposed to leverage the low power of PCM and high speed of DRAM. In this paper, we propose energy-aware real-time task scheduling strategies for hybrid PCM-DRAM based embedded systems. Given the execution time variation when a task is loaded into PCM or DRAM, we re-design the static table-driven scheduling for a set of fixed tasks, as well as the Rate-Monotonic (RM) and Earliest Deadline First (EDF) scheduling policies for periodic task sets. Furthermore, since the actual execution time can be much shorter than the worst-case execution time in the actual execution, we propose online schedulers which migrates the tasks between PCM and DRAM to optimize the energy consumption by utilizing the slack time resulted from the completed tasks. All the proposed algorithms minimize the number of task migrations from PCM to DRAM by ensuring that aperiodic tasks are not migrated while each periodic task instance can be migrated at most once. Experimental results show our proposed scheduling algorithms satisfy the real-time constraints and significantly reduce the energy consumption.

Energy minimization for real-time systems with (m,k)-guarantee

Energy consumption and quality of service (QoS) are two primary concerns in the development of today's pervasive computing systems. While most of the current research in energy-aware real-time scheduling has been focused on hard real-time systems, a large number of practical applications and systems exhibit more soft real-time nature. In this paper, we study the problem of minimizing energy for soft real-time systems while providing a QoS guarantee. The QoS requirements are deterministically quantified with the (m,k)-constraints, which require that at least m out of any k consecutive jobs of a task meet their deadlines. In this paper, we propose a hybrid approach to achieve the dual goals of QoS guarantee and energy minimization. We first present the necessary and sufficient schedulability conditions for the static mandatory/optional workload partitioning. Then, we propose to dynamically vary the statically defined mandatory/optional partitions to accommodate dynamic run-time variations while minimizing the energy consumption. The experimental results demonstrate that our proposed techniques outperform previous work significantly in terms of both the energy savings and achieved QoS.